**Machine Learning** is about *using the right features, to build the right models, to achieve the right tasks* [[Flach, 2012]][0] However, to come up with a definition of what actually means **right** for the problem at the hand, it is required to analyse huge amounts of data, and to evaluate the performance of different algorithms on these data. However, deriving a working machine learning solution for a given problem is far from being a *waterfall* process. It is an iterative process where...
Can empirical samples unveil the big picture? Is chess games descriptions expose good enough data to gain understanding of chess rules - legal piece moves, castling, check versus checkmate, etc. Which features are important in describing a chess game and which features are not. What is a good representation of a chess game for this uses. What is the minimal sample size which is required in order to learn this in a good enough manner and where this learning can go wrong. **Ne3 => E=mc2**...
This talk will give a basic introduction to the pySPACE framework and its current applications. pySPACE (Signal Processing And Classification Environment) is a modular software for the processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial...
Topics: python, pydata, pydataberlin2014
